12-pandas/12.01-ten-minutes-to-pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 十分钟上手 Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`pandas` 是一个 `Python Data Analysis Library`。\n",
    "\n",
    "安装请参考官网的教程，如果安装了 `Anaconda`，则不需要安装 `pandas` 库。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 产生 Pandas 对象"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`pandas` 中有三种基本结构：\n",
    "\n",
    "- `Series`\n",
    "    - 1D labeled homogeneously-typed array\n",
    "- `DataFrame`\n",
    "    - General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed columns\n",
    "- `Panel`\n",
    "    - General 3D labeled, also size-mutable array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "一维 `Series` 可以用一维列表初始化："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     1\n",
      "1     3\n",
      "2     5\n",
      "3   NaN\n",
      "4     6\n",
      "5     8\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "s = pd.Series([1,3,5,np.nan,6,8])\n",
    "\n",
    "print s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "默认情况下，`Series` 的下标都是数字（可以使用额外参数指定），类型是统一的。\n",
    "\n",
    "### DataFrame\n",
    "\n",
    "`DataFrame` 则是个二维结构，这里首先构造一组时间序列，作为我们第一维的下标："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
      "               '2013-01-05', '2013-01-06'],\n",
      "              dtype='datetime64[ns]', freq='D')\n"
     ]
    }
   ],
   "source": [
    "dates = pd.date_range('20130101', periods=6)\n",
    "\n",
    "print dates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后创建一个 `DataFrame` 结构："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.605936</td>\n",
       "      <td>-0.861658</td>\n",
       "      <td>-1.001924</td>\n",
       "      <td>1.528584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.165408</td>\n",
       "      <td>0.388338</td>\n",
       "      <td>1.187187</td>\n",
       "      <td>1.819818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.065255</td>\n",
       "      <td>-1.608074</td>\n",
       "      <td>-1.282331</td>\n",
       "      <td>-0.286067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.289305</td>\n",
       "      <td>0.497115</td>\n",
       "      <td>-0.225351</td>\n",
       "      <td>0.040239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.038232</td>\n",
       "      <td>0.875057</td>\n",
       "      <td>-0.092526</td>\n",
       "      <td>0.934432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-2.163453</td>\n",
       "      <td>-0.010279</td>\n",
       "      <td>1.699886</td>\n",
       "      <td>1.291653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01 -0.605936 -0.861658 -1.001924  1.528584\n",
       "2013-01-02 -0.165408  0.388338  1.187187  1.819818\n",
       "2013-01-03  0.065255 -1.608074 -1.282331 -0.286067\n",
       "2013-01-04  1.289305  0.497115 -0.225351  0.040239\n",
       "2013-01-05  0.038232  0.875057 -0.092526  0.934432\n",
       "2013-01-06 -2.163453 -0.010279  1.699886  1.291653"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "默认情况下，如果不指定 `index` 参数和 `columns`，那么他们的值将用从 `0` 开始的数字替代。\n",
    "\n",
    "除了向 `DataFrame` 中传入二维数组，我们也可以使用字典传入数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A          B  C  D      E    F\n",
       "0  1 2013-01-02  1  3   test  foo\n",
       "1  1 2013-01-02  1  3  train  foo\n",
       "2  1 2013-01-02  1  3   test  foo\n",
       "3  1 2013-01-02  1  3  train  foo"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'A' : 1.,\n",
    "                    'B' : pd.Timestamp('20130102'),\n",
    "                    'C' : pd.Series(1,index=list(range(4)),dtype='float32'),\n",
    "                    'D' : np.array([3] * 4,dtype='int32'),\n",
    "                    'E' : pd.Categorical([\"test\",\"train\",\"test\",\"train\"]),\n",
    "                    'F' : 'foo' })\n",
    "\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "字典的每个 `key` 代表一列，其 `value` 可以是各种能够转化为 `Series` 的对象。\n",
    "\n",
    "与 `Series` 要求所有的类型都一致不同，`DataFrame` 值要求每一列数据的格式相同："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A           float64\n",
       "B    datetime64[ns]\n",
       "C           float32\n",
       "D             int32\n",
       "E          category\n",
       "F            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 头尾数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`head` 和 `tail` 方法可以分别查看最前面几行和最后面几行的数据（默认为 5）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.605936</td>\n",
       "      <td>-0.861658</td>\n",
       "      <td>-1.001924</td>\n",
       "      <td>1.528584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.165408</td>\n",
       "      <td>0.388338</td>\n",
       "      <td>1.187187</td>\n",
       "      <td>1.819818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.065255</td>\n",
       "      <td>-1.608074</td>\n",
       "      <td>-1.282331</td>\n",
       "      <td>-0.286067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.289305</td>\n",
       "      <td>0.497115</td>\n",
       "      <td>-0.225351</td>\n",
       "      <td>0.040239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.038232</td>\n",
       "      <td>0.875057</td>\n",
       "      <td>-0.092526</td>\n",
       "      <td>0.934432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01 -0.605936 -0.861658 -1.001924  1.528584\n",
       "2013-01-02 -0.165408  0.388338  1.187187  1.819818\n",
       "2013-01-03  0.065255 -1.608074 -1.282331 -0.286067\n",
       "2013-01-04  1.289305  0.497115 -0.225351  0.040239\n",
       "2013-01-05  0.038232  0.875057 -0.092526  0.934432"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最后 3 行："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.289305</td>\n",
       "      <td>0.497115</td>\n",
       "      <td>-0.225351</td>\n",
       "      <td>0.040239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.038232</td>\n",
       "      <td>0.875057</td>\n",
       "      <td>-0.092526</td>\n",
       "      <td>0.934432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-2.163453</td>\n",
       "      <td>-0.010279</td>\n",
       "      <td>1.699886</td>\n",
       "      <td>1.291653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-04  1.289305  0.497115 -0.225351  0.040239\n",
       "2013-01-05  0.038232  0.875057 -0.092526  0.934432\n",
       "2013-01-06 -2.163453 -0.010279  1.699886  1.291653"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 下标，列标，数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下标使用 `index` 属性查看："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
       "               '2013-01-05', '2013-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "列标使用 `columns` 属性查看："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'A', u'B', u'C', u'D'], dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据值使用 `values` 查看："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.60593585, -0.86165752, -1.00192387,  1.52858443],\n",
       "       [-0.16540784,  0.38833783,  1.18718697,  1.81981793],\n",
       "       [ 0.06525454, -1.60807414, -1.2823306 , -0.28606716],\n",
       "       [ 1.28930486,  0.49711531, -0.22535143,  0.04023897],\n",
       "       [ 0.03823179,  0.87505664, -0.0925258 ,  0.93443212],\n",
       "       [-2.16345271, -0.01027865,  1.69988608,  1.29165337]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 统计数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看简单的统计数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.257001</td>\n",
       "      <td>-0.119917</td>\n",
       "      <td>0.047490</td>\n",
       "      <td>0.888110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.126657</td>\n",
       "      <td>0.938705</td>\n",
       "      <td>1.182629</td>\n",
       "      <td>0.841529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.163453</td>\n",
       "      <td>-1.608074</td>\n",
       "      <td>-1.282331</td>\n",
       "      <td>-0.286067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.495804</td>\n",
       "      <td>-0.648813</td>\n",
       "      <td>-0.807781</td>\n",
       "      <td>0.263787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.063588</td>\n",
       "      <td>0.189030</td>\n",
       "      <td>-0.158939</td>\n",
       "      <td>1.113043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.058499</td>\n",
       "      <td>0.469921</td>\n",
       "      <td>0.867259</td>\n",
       "      <td>1.469352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.289305</td>\n",
       "      <td>0.875057</td>\n",
       "      <td>1.699886</td>\n",
       "      <td>1.819818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              A         B         C         D\n",
       "count  6.000000  6.000000  6.000000  6.000000\n",
       "mean  -0.257001 -0.119917  0.047490  0.888110\n",
       "std    1.126657  0.938705  1.182629  0.841529\n",
       "min   -2.163453 -1.608074 -1.282331 -0.286067\n",
       "25%   -0.495804 -0.648813 -0.807781  0.263787\n",
       "50%   -0.063588  0.189030 -0.158939  1.113043\n",
       "75%    0.058499  0.469921  0.867259  1.469352\n",
       "max    1.289305  0.875057  1.699886  1.819818"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 转置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2013-01-01 00:00:00</th>\n",
       "      <th>2013-01-02 00:00:00</th>\n",
       "      <th>2013-01-03 00:00:00</th>\n",
       "      <th>2013-01-04 00:00:00</th>\n",
       "      <th>2013-01-05 00:00:00</th>\n",
       "      <th>2013-01-06 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>-0.605936</td>\n",
       "      <td>-0.165408</td>\n",
       "      <td>0.065255</td>\n",
       "      <td>1.289305</td>\n",
       "      <td>0.038232</td>\n",
       "      <td>-2.163453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.861658</td>\n",
       "      <td>0.388338</td>\n",
       "      <td>-1.608074</td>\n",
       "      <td>0.497115</td>\n",
       "      <td>0.875057</td>\n",
       "      <td>-0.010279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-1.001924</td>\n",
       "      <td>1.187187</td>\n",
       "      <td>-1.282331</td>\n",
       "      <td>-0.225351</td>\n",
       "      <td>-0.092526</td>\n",
       "      <td>1.699886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>1.528584</td>\n",
       "      <td>1.819818</td>\n",
       "      <td>-0.286067</td>\n",
       "      <td>0.040239</td>\n",
       "      <td>0.934432</td>\n",
       "      <td>1.291653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2013-01-01  2013-01-02  2013-01-03  2013-01-04  2013-01-05  2013-01-06\n",
       "A   -0.605936   -0.165408    0.065255    1.289305    0.038232   -2.163453\n",
       "B   -0.861658    0.388338   -1.608074    0.497115    0.875057   -0.010279\n",
       "C   -1.001924    1.187187   -1.282331   -0.225351   -0.092526    1.699886\n",
       "D    1.528584    1.819818   -0.286067    0.040239    0.934432    1.291653"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 排序"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`sort_index(axis=0, ascending=True)` 方法按照下标大小进行排序，`axis=0` 表示按第 0 维进行排序。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-2.163453</td>\n",
       "      <td>-0.010279</td>\n",
       "      <td>1.699886</td>\n",
       "      <td>1.291653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.038232</td>\n",
       "      <td>0.875057</td>\n",
       "      <td>-0.092526</td>\n",
       "      <td>0.934432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.289305</td>\n",
       "      <td>0.497115</td>\n",
       "      <td>-0.225351</td>\n",
       "      <td>0.040239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.065255</td>\n",
       "      <td>-1.608074</td>\n",
       "      <td>-1.282331</td>\n",
       "      <td>-0.286067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.165408</td>\n",
       "      <td>0.388338</td>\n",
       "      <td>1.187187</td>\n",
       "      <td>1.819818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.605936</td>\n",
       "      <td>-0.861658</td>\n",
       "      <td>-1.001924</td>\n",
       "      <td>1.528584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-06 -2.163453 -0.010279  1.699886  1.291653\n",
       "2013-01-05  0.038232  0.875057 -0.092526  0.934432\n",
       "2013-01-04  1.289305  0.497115 -0.225351  0.040239\n",
       "2013-01-03  0.065255 -1.608074 -1.282331 -0.286067\n",
       "2013-01-02 -0.165408  0.388338  1.187187  1.819818\n",
       "2013-01-01 -0.605936 -0.861658 -1.001924  1.528584"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>D</th>\n",
       "      <th>C</th>\n",
       "      <th>B</th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>1.528584</td>\n",
       "      <td>-1.001924</td>\n",
       "      <td>-0.861658</td>\n",
       "      <td>-0.605936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.819818</td>\n",
       "      <td>1.187187</td>\n",
       "      <td>0.388338</td>\n",
       "      <td>-0.165408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.286067</td>\n",
       "      <td>-1.282331</td>\n",
       "      <td>-1.608074</td>\n",
       "      <td>0.065255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.040239</td>\n",
       "      <td>-0.225351</td>\n",
       "      <td>0.497115</td>\n",
       "      <td>1.289305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.934432</td>\n",
       "      <td>-0.092526</td>\n",
       "      <td>0.875057</td>\n",
       "      <td>0.038232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>1.291653</td>\n",
       "      <td>1.699886</td>\n",
       "      <td>-0.010279</td>\n",
       "      <td>-2.163453</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   D         C         B         A\n",
       "2013-01-01  1.528584 -1.001924 -0.861658 -0.605936\n",
       "2013-01-02  1.819818  1.187187  0.388338 -0.165408\n",
       "2013-01-03 -0.286067 -1.282331 -1.608074  0.065255\n",
       "2013-01-04  0.040239 -0.225351  0.497115  1.289305\n",
       "2013-01-05  0.934432 -0.092526  0.875057  0.038232\n",
       "2013-01-06  1.291653  1.699886 -0.010279 -2.163453"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis=1, ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`sort_values(by, axis=0, ascending=True)` 方法按照 `by` 的值的大小进行排序，例如按照 `B` 列的大小："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.065255</td>\n",
       "      <td>-1.608074</td>\n",
       "      <td>-1.282331</td>\n",
       "      <td>-0.286067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.605936</td>\n",
       "      <td>-0.861658</td>\n",
       "      <td>-1.001924</td>\n",
       "      <td>1.528584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-2.163453</td>\n",
       "      <td>-0.010279</td>\n",
       "      <td>1.699886</td>\n",
       "      <td>1.291653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.165408</td>\n",
       "      <td>0.388338</td>\n",
       "      <td>1.187187</td>\n",
       "      <td>1.819818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.289305</td>\n",
       "      <td>0.497115</td>\n",
       "      <td>-0.225351</td>\n",
       "      <td>0.040239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.038232</td>\n",
       "      <td>0.875057</td>\n",
       "      <td>-0.092526</td>\n",
       "      <td>0.934432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-03  0.065255 -1.608074 -1.282331 -0.286067\n",
       "2013-01-01 -0.605936 -0.861658 -1.001924  1.528584\n",
       "2013-01-06 -2.163453 -0.010279  1.699886  1.291653\n",
       "2013-01-02 -0.165408  0.388338  1.187187  1.819818\n",
       "2013-01-04  1.289305  0.497115 -0.225351  0.040239\n",
       "2013-01-05  0.038232  0.875057 -0.092526  0.934432"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=\"B\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 索引"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "虽然 `DataFrame` 支持 `Python/Numpy` 的索引语法，但是推荐使用 `.at, .iat, .loc, .iloc 和 .ix` 方法进行索引。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读取数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "选择单列数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01   -0.605936\n",
       "2013-01-02   -0.165408\n",
       "2013-01-03    0.065255\n",
       "2013-01-04    1.289305\n",
       "2013-01-05    0.038232\n",
       "2013-01-06   -2.163453\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"A\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "也可以用 `df.A`："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01   -0.605936\n",
       "2013-01-02   -0.165408\n",
       "2013-01-03    0.065255\n",
       "2013-01-04    1.289305\n",
       "2013-01-05    0.038232\n",
       "2013-01-06   -2.163453\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用切片读取多行："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.605936</td>\n",
       "      <td>-0.861658</td>\n",
       "      <td>-1.001924</td>\n",
       "      <td>1.528584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.165408</td>\n",
       "      <td>0.388338</td>\n",
       "      <td>1.187187</td>\n",
       "      <td>1.819818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.065255</td>\n",
       "      <td>-1.608074</td>\n",
       "      <td>-1.282331</td>\n",
       "      <td>-0.286067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01 -0.605936 -0.861658 -1.001924  1.528584\n",
       "2013-01-02 -0.165408  0.388338  1.187187  1.819818\n",
       "2013-01-03  0.065255 -1.608074 -1.282331 -0.286067"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`index` 名字也可以进行切片："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.605936</td>\n",
       "      <td>-0.861658</td>\n",
       "      <td>-1.001924</td>\n",
       "      <td>1.528584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.165408</td>\n",
       "      <td>0.388338</td>\n",
       "      <td>1.187187</td>\n",
       "      <td>1.819818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.065255</td>\n",
       "      <td>-1.608074</td>\n",
       "      <td>-1.282331</td>\n",
       "      <td>-0.286067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01 -0.605936 -0.861658 -1.001924  1.528584\n",
       "2013-01-02 -0.165408  0.388338  1.187187  1.819818\n",
       "2013-01-03  0.065255 -1.608074 -1.282331 -0.286067"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"20130101\":\"20130103\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用 `label` 索引"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`loc` 可以方便的使用 `label` 进行索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.605936\n",
       "B   -0.861658\n",
       "C   -1.001924\n",
       "D    1.528584\n",
       "Name: 2013-01-01 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "多列数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.605936</td>\n",
       "      <td>-0.861658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.165408</td>\n",
       "      <td>0.388338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.065255</td>\n",
       "      <td>-1.608074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.289305</td>\n",
       "      <td>0.497115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.038232</td>\n",
       "      <td>0.875057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-2.163453</td>\n",
       "      <td>-0.010279</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2013-01-01 -0.605936 -0.861658\n",
       "2013-01-02 -0.165408  0.388338\n",
       "2013-01-03  0.065255 -1.608074\n",
       "2013-01-04  1.289305  0.497115\n",
       "2013-01-05  0.038232  0.875057\n",
       "2013-01-06 -2.163453 -0.010279"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,['A','B']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "选择多行多列："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.165408</td>\n",
       "      <td>0.388338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.065255</td>\n",
       "      <td>-1.608074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.289305</td>\n",
       "      <td>0.497115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2013-01-02 -0.165408  0.388338\n",
       "2013-01-03  0.065255 -1.608074\n",
       "2013-01-04  1.289305  0.497115"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['20130102':'20130104',['A','B']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据降维："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.165408\n",
       "B    0.388338\n",
       "Name: 2013-01-02 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['20130102',['A','B']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "得到标量值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.86165751902832299"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[0],'B']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不过得到标量值可以用 `at`，速度更快："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 329 µs per loop\n",
      "100 loops, best of 3: 31.1 µs per loop\n",
      "-0.861657519028\n"
     ]
    }
   ],
   "source": [
    "%timeit -n100 df.loc[dates[0],'B']\n",
    "%timeit -n100 df.at[dates[0],'B']\n",
    "\n",
    "print df.at[dates[0],'B']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用位置索引"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`iloc` 使用位置进行索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    1.289305\n",
       "B    0.497115\n",
       "C   -0.225351\n",
       "D    0.040239\n",
       "Name: 2013-01-04 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "连续切片："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.289305</td>\n",
       "      <td>0.497115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.038232</td>\n",
       "      <td>0.875057</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2013-01-04  1.289305  0.497115\n",
       "2013-01-05  0.038232  0.875057"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3:5,0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "索引不连续的部分："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.165408</td>\n",
       "      <td>1.187187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.065255</td>\n",
       "      <td>-1.282331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.038232</td>\n",
       "      <td>-0.092526</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         C\n",
       "2013-01-02 -0.165408  1.187187\n",
       "2013-01-03  0.065255 -1.282331\n",
       "2013-01-05  0.038232 -0.092526"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[1,2,4],[0,2]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "索引整行："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.165408</td>\n",
       "      <td>0.388338</td>\n",
       "      <td>1.187187</td>\n",
       "      <td>1.819818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.065255</td>\n",
       "      <td>-1.608074</td>\n",
       "      <td>-1.282331</td>\n",
       "      <td>-0.286067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-02 -0.165408  0.388338  1.187187  1.819818\n",
       "2013-01-03  0.065255 -1.608074 -1.282331 -0.286067"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "整列："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.861658</td>\n",
       "      <td>-1.001924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.388338</td>\n",
       "      <td>1.187187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-1.608074</td>\n",
       "      <td>-1.282331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.497115</td>\n",
       "      <td>-0.225351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.875057</td>\n",
       "      <td>-0.092526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-0.010279</td>\n",
       "      <td>1.699886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   B         C\n",
       "2013-01-01 -0.861658 -1.001924\n",
       "2013-01-02  0.388338  1.187187\n",
       "2013-01-03 -1.608074 -1.282331\n",
       "2013-01-04  0.497115 -0.225351\n",
       "2013-01-05  0.875057 -0.092526\n",
       "2013-01-06 -0.010279  1.699886"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:, 1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "标量值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3883378290420279"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当然，使用 `iat` 索引标量值更快："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 236 µs per loop\n",
      "100 loops, best of 3: 14.5 µs per loop\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.3883378290420279"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%timeit -n100 df.iloc[1,1]\n",
    "%timeit -n100 df.iat[1,1]\n",
    "\n",
    "df.iat[1,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 布尔型索引"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "所有 `A` 列大于 0 的行："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.065255</td>\n",
       "      <td>-1.608074</td>\n",
       "      <td>-1.282331</td>\n",
       "      <td>-0.286067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.289305</td>\n",
       "      <td>0.497115</td>\n",
       "      <td>-0.225351</td>\n",
       "      <td>0.040239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.038232</td>\n",
       "      <td>0.875057</td>\n",
       "      <td>-0.092526</td>\n",
       "      <td>0.934432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-03  0.065255 -1.608074 -1.282331 -0.286067\n",
       "2013-01-04  1.289305  0.497115 -0.225351  0.040239\n",
       "2013-01-05  0.038232  0.875057 -0.092526  0.934432"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.A > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "只留下所有大于 0 的数值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.528584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.388338</td>\n",
       "      <td>1.187187</td>\n",
       "      <td>1.819818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.065255</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.289305</td>\n",
       "      <td>0.497115</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.040239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.038232</td>\n",
       "      <td>0.875057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.934432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.699886</td>\n",
       "      <td>1.291653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01       NaN       NaN       NaN  1.528584\n",
       "2013-01-02       NaN  0.388338  1.187187  1.819818\n",
       "2013-01-03  0.065255       NaN       NaN       NaN\n",
       "2013-01-04  1.289305  0.497115       NaN  0.040239\n",
       "2013-01-05  0.038232  0.875057       NaN  0.934432\n",
       "2013-01-06       NaN       NaN  1.699886  1.291653"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用 `isin` 方法做 `filter` 过滤："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.605936</td>\n",
       "      <td>-0.861658</td>\n",
       "      <td>-1.001924</td>\n",
       "      <td>1.528584</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.165408</td>\n",
       "      <td>0.388338</td>\n",
       "      <td>1.187187</td>\n",
       "      <td>1.819818</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.065255</td>\n",
       "      <td>-1.608074</td>\n",
       "      <td>-1.282331</td>\n",
       "      <td>-0.286067</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.289305</td>\n",
       "      <td>0.497115</td>\n",
       "      <td>-0.225351</td>\n",
       "      <td>0.040239</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.038232</td>\n",
       "      <td>0.875057</td>\n",
       "      <td>-0.092526</td>\n",
       "      <td>0.934432</td>\n",
       "      <td>four</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-2.163453</td>\n",
       "      <td>-0.010279</td>\n",
       "      <td>1.699886</td>\n",
       "      <td>1.291653</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D      E\n",
       "2013-01-01 -0.605936 -0.861658 -1.001924  1.528584    one\n",
       "2013-01-02 -0.165408  0.388338  1.187187  1.819818    one\n",
       "2013-01-03  0.065255 -1.608074 -1.282331 -0.286067    two\n",
       "2013-01-04  1.289305  0.497115 -0.225351  0.040239  three\n",
       "2013-01-05  0.038232  0.875057 -0.092526  0.934432   four\n",
       "2013-01-06 -2.163453 -0.010279  1.699886  1.291653  three"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.copy()\n",
    "df2['E'] = ['one', 'one','two','three','four','three']\n",
    "\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.065255</td>\n",
       "      <td>-1.608074</td>\n",
       "      <td>-1.282331</td>\n",
       "      <td>-0.286067</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.038232</td>\n",
       "      <td>0.875057</td>\n",
       "      <td>-0.092526</td>\n",
       "      <td>0.934432</td>\n",
       "      <td>four</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D     E\n",
       "2013-01-03  0.065255 -1.608074 -1.282331 -0.286067   two\n",
       "2013-01-05  0.038232  0.875057 -0.092526  0.934432  four"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2['E'].isin(['two','four'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 设定数据的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-02    1\n",
       "2013-01-03    2\n",
       "2013-01-04    3\n",
       "2013-01-05    4\n",
       "2013-01-06    5\n",
       "2013-01-07    6\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series([1,2,3,4,5,6], index=pd.date_range('20130102', periods=6))\n",
    "\n",
    "s1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "像字典一样，直接指定 `F` 列的值为 `s1`，此时以 `df` 已有的 `index` 为标准将二者进行合并，`s1` 中没有的 `index` 项设为 `NaN`，多余的项舍去："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.605936</td>\n",
       "      <td>-0.861658</td>\n",
       "      <td>-1.001924</td>\n",
       "      <td>1.528584</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.165408</td>\n",
       "      <td>0.388338</td>\n",
       "      <td>1.187187</td>\n",
       "      <td>1.819818</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.065255</td>\n",
       "      <td>-1.608074</td>\n",
       "      <td>-1.282331</td>\n",
       "      <td>-0.286067</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.289305</td>\n",
       "      <td>0.497115</td>\n",
       "      <td>-0.225351</td>\n",
       "      <td>0.040239</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.038232</td>\n",
       "      <td>0.875057</td>\n",
       "      <td>-0.092526</td>\n",
       "      <td>0.934432</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-2.163453</td>\n",
       "      <td>-0.010279</td>\n",
       "      <td>1.699886</td>\n",
       "      <td>1.291653</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D   F\n",
       "2013-01-01 -0.605936 -0.861658 -1.001924  1.528584 NaN\n",
       "2013-01-02 -0.165408  0.388338  1.187187  1.819818   1\n",
       "2013-01-03  0.065255 -1.608074 -1.282331 -0.286067   2\n",
       "2013-01-04  1.289305  0.497115 -0.225351  0.040239   3\n",
       "2013-01-05  0.038232  0.875057 -0.092526  0.934432   4\n",
       "2013-01-06 -2.163453 -0.010279  1.699886  1.291653   5"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['F'] = s1\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "或者使用 `at` 或 `iat` 修改单个值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.861658</td>\n",
       "      <td>-1.001924</td>\n",
       "      <td>1.528584</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.165408</td>\n",
       "      <td>0.388338</td>\n",
       "      <td>1.187187</td>\n",
       "      <td>1.819818</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.065255</td>\n",
       "      <td>-1.608074</td>\n",
       "      <td>-1.282331</td>\n",
       "      <td>-0.286067</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.289305</td>\n",
       "      <td>0.497115</td>\n",
       "      <td>-0.225351</td>\n",
       "      <td>0.040239</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.038232</td>\n",
       "      <td>0.875057</td>\n",
       "      <td>-0.092526</td>\n",
       "      <td>0.934432</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-2.163453</td>\n",
       "      <td>-0.010279</td>\n",
       "      <td>1.699886</td>\n",
       "      <td>1.291653</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D   F\n",
       "2013-01-01  0.000000 -0.861658 -1.001924  1.528584 NaN\n",
       "2013-01-02 -0.165408  0.388338  1.187187  1.819818   1\n",
       "2013-01-03  0.065255 -1.608074 -1.282331 -0.286067   2\n",
       "2013-01-04  1.289305  0.497115 -0.225351  0.040239   3\n",
       "2013-01-05  0.038232  0.875057 -0.092526  0.934432   4\n",
       "2013-01-06 -2.163453 -0.010279  1.699886  1.291653   5"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.at[dates[0],'A'] = 0\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.001924</td>\n",
       "      <td>1.528584</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.165408</td>\n",
       "      <td>0.388338</td>\n",
       "      <td>1.187187</td>\n",
       "      <td>1.819818</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.065255</td>\n",
       "      <td>-1.608074</td>\n",
       "      <td>-1.282331</td>\n",
       "      <td>-0.286067</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.289305</td>\n",
       "      <td>0.497115</td>\n",
       "      <td>-0.225351</td>\n",
       "      <td>0.040239</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.038232</td>\n",
       "      <td>0.875057</td>\n",
       "      <td>-0.092526</td>\n",
       "      <td>0.934432</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-2.163453</td>\n",
       "      <td>-0.010279</td>\n",
       "      <td>1.699886</td>\n",
       "      <td>1.291653</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D   F\n",
       "2013-01-01  0.000000  0.000000 -1.001924  1.528584 NaN\n",
       "2013-01-02 -0.165408  0.388338  1.187187  1.819818   1\n",
       "2013-01-03  0.065255 -1.608074 -1.282331 -0.286067   2\n",
       "2013-01-04  1.289305  0.497115 -0.225351  0.040239   3\n",
       "2013-01-05  0.038232  0.875057 -0.092526  0.934432   4\n",
       "2013-01-06 -2.163453 -0.010279  1.699886  1.291653   5"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iat[0, 1] = 0\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "设定一整列："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.001924</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.165408</td>\n",
       "      <td>0.388338</td>\n",
       "      <td>1.187187</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.065255</td>\n",
       "      <td>-1.608074</td>\n",
       "      <td>-1.282331</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.289305</td>\n",
       "      <td>0.497115</td>\n",
       "      <td>-0.225351</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.038232</td>\n",
       "      <td>0.875057</td>\n",
       "      <td>-0.092526</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-2.163453</td>\n",
       "      <td>-0.010279</td>\n",
       "      <td>1.699886</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D   F\n",
       "2013-01-01  0.000000  0.000000 -1.001924  5 NaN\n",
       "2013-01-02 -0.165408  0.388338  1.187187  5   1\n",
       "2013-01-03  0.065255 -1.608074 -1.282331  5   2\n",
       "2013-01-04  1.289305  0.497115 -0.225351  5   3\n",
       "2013-01-05  0.038232  0.875057 -0.092526  5   4\n",
       "2013-01-06 -2.163453 -0.010279  1.699886  5   5"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,'D'] = np.array([5] * len(df))\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "设定满足条件的数值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.001924</td>\n",
       "      <td>-5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.165408</td>\n",
       "      <td>-0.388338</td>\n",
       "      <td>-1.187187</td>\n",
       "      <td>-5</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.065255</td>\n",
       "      <td>-1.608074</td>\n",
       "      <td>-1.282331</td>\n",
       "      <td>-5</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-1.289305</td>\n",
       "      <td>-0.497115</td>\n",
       "      <td>-0.225351</td>\n",
       "      <td>-5</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-0.038232</td>\n",
       "      <td>-0.875057</td>\n",
       "      <td>-0.092526</td>\n",
       "      <td>-5</td>\n",
       "      <td>-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-2.163453</td>\n",
       "      <td>-0.010279</td>\n",
       "      <td>-1.699886</td>\n",
       "      <td>-5</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D   F\n",
       "2013-01-01  0.000000  0.000000 -1.001924 -5 NaN\n",
       "2013-01-02 -0.165408 -0.388338 -1.187187 -5  -1\n",
       "2013-01-03 -0.065255 -1.608074 -1.282331 -5  -2\n",
       "2013-01-04 -1.289305 -0.497115 -0.225351 -5  -3\n",
       "2013-01-05 -0.038232 -0.875057 -0.092526 -5  -4\n",
       "2013-01-06 -2.163453 -0.010279 -1.699886 -5  -5"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.copy()\n",
    "\n",
    "df2[df2 > 0] = -df2\n",
    "\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 缺失数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.001924</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.165408</td>\n",
       "      <td>0.388338</td>\n",
       "      <td>1.187187</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.065255</td>\n",
       "      <td>-1.608074</td>\n",
       "      <td>-1.282331</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.289305</td>\n",
       "      <td>0.497115</td>\n",
       "      <td>-0.225351</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D   F   E\n",
       "2013-01-01  0.000000  0.000000 -1.001924  5 NaN   1\n",
       "2013-01-02 -0.165408  0.388338  1.187187  5   1   1\n",
       "2013-01-03  0.065255 -1.608074 -1.282331  5   2 NaN\n",
       "2013-01-04  1.289305  0.497115 -0.225351  5   3 NaN"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])\n",
    "df1.loc[dates[0]:dates[1],'E'] = 1\n",
    "\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "丢弃所有缺失数据的行得到的新数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.165408</td>\n",
       "      <td>0.388338</td>\n",
       "      <td>1.187187</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D  F  E\n",
       "2013-01-02 -0.165408  0.388338  1.187187  5  1  1"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dropna(how='any')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "填充缺失数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.001924</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.165408</td>\n",
       "      <td>0.388338</td>\n",
       "      <td>1.187187</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.065255</td>\n",
       "      <td>-1.608074</td>\n",
       "      <td>-1.282331</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.289305</td>\n",
       "      <td>0.497115</td>\n",
       "      <td>-0.225351</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D  F  E\n",
       "2013-01-01  0.000000  0.000000 -1.001924  5  5  1\n",
       "2013-01-02 -0.165408  0.388338  1.187187  5  1  1\n",
       "2013-01-03  0.065255 -1.608074 -1.282331  5  2  5\n",
       "2013-01-04  1.289305  0.497115 -0.225351  5  3  5"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.fillna(value=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "检查缺失数据的位置："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                A      B      C      D      F      E\n",
       "2013-01-01  False  False  False  False   True  False\n",
       "2013-01-02  False  False  False  False  False  False\n",
       "2013-01-03  False  False  False  False  False   True\n",
       "2013-01-04  False  False  False  False  False   True"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(df1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 计算操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 统计信息"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "每一列的均值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.156012\n",
       "B    0.023693\n",
       "C    0.047490\n",
       "D    5.000000\n",
       "F    3.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "每一行的均值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01    0.999519\n",
       "2013-01-02    1.482023\n",
       "2013-01-03    0.834970\n",
       "2013-01-04    1.912214\n",
       "2013-01-05    1.964153\n",
       "2013-01-06    1.905231\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "多个对象之间的操作，如果维度不对，`pandas` 会自动调用 `broadcasting` 机制："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2013-01-01   NaN\n",
      "2013-01-02   NaN\n",
      "2013-01-03     1\n",
      "2013-01-04     3\n",
      "2013-01-05     5\n",
      "2013-01-06   NaN\n",
      "Freq: D, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "s = pd.Series([1,3,5,np.nan,6,8], index=dates).shift(2)\n",
    "\n",
    "print s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "相减 `df - s`："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.934745</td>\n",
       "      <td>-2.608074</td>\n",
       "      <td>-2.282331</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-1.710695</td>\n",
       "      <td>-2.502885</td>\n",
       "      <td>-3.225351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-4.961768</td>\n",
       "      <td>-4.124943</td>\n",
       "      <td>-5.092526</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C   D   F\n",
       "2013-01-01       NaN       NaN       NaN NaN NaN\n",
       "2013-01-02       NaN       NaN       NaN NaN NaN\n",
       "2013-01-03 -0.934745 -2.608074 -2.282331   4   1\n",
       "2013-01-04 -1.710695 -2.502885 -3.225351   2   0\n",
       "2013-01-05 -4.961768 -4.124943 -5.092526   0  -1\n",
       "2013-01-06       NaN       NaN       NaN NaN NaN"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sub(s, axis='index')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### apply 操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "与 `R` 中的 `apply` 操作类似，接收一个函数，默认是对将函数作用到每一列上："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.001924</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.165408</td>\n",
       "      <td>0.388338</td>\n",
       "      <td>0.185263</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.100153</td>\n",
       "      <td>-1.219736</td>\n",
       "      <td>-1.097067</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.189152</td>\n",
       "      <td>-0.722621</td>\n",
       "      <td>-1.322419</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.227383</td>\n",
       "      <td>0.152436</td>\n",
       "      <td>-1.414945</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-0.936069</td>\n",
       "      <td>0.142157</td>\n",
       "      <td>0.284941</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C   D   F\n",
       "2013-01-01  0.000000  0.000000 -1.001924   5 NaN\n",
       "2013-01-02 -0.165408  0.388338  0.185263  10   1\n",
       "2013-01-03 -0.100153 -1.219736 -1.097067  15   3\n",
       "2013-01-04  1.189152 -0.722621 -1.322419  20   6\n",
       "2013-01-05  1.227383  0.152436 -1.414945  25  10\n",
       "2013-01-06 -0.936069  0.142157  0.284941  30  15"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(np.cumsum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "求每列最大最小值之差："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    3.452758\n",
       "B    2.483131\n",
       "C    2.982217\n",
       "D    0.000000\n",
       "F    4.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(lambda x: x.max() - x.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    2\n",
      "1    5\n",
      "2    6\n",
      "3    6\n",
      "4    6\n",
      "5    3\n",
      "6    5\n",
      "7    0\n",
      "8    4\n",
      "9    4\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "s = pd.Series(np.random.randint(0, 7, size=10))\n",
    "print s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "直方图信息："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6    3\n",
      "5    2\n",
      "4    2\n",
      "3    1\n",
      "2    1\n",
      "0    1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print s.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "绘制直方图信息："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BU4ALJH10wKb3RcSG+uOKFurMnjNyy1eVuoCWVakLSG7YGfkZwNMRsSciXgNuAT4zYLtF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\nAwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3dc7d65d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = s.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 字符串方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当 `Series` 或者 `DataFrame` 的某一列是字符串时，我们可以用 `.str` 对这个字符串数组进行字符串的基本操作： "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       a\n",
      "1       b\n",
      "2       c\n",
      "3    aaba\n",
      "4    baca\n",
      "5     NaN\n",
      "6    caba\n",
      "7     dog\n",
      "8     cat\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])\n",
    "\n",
    "print s.str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 合并"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 连接"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.346373</td>\n",
       "      <td>0.105651</td>\n",
       "      <td>-0.048027</td>\n",
       "      <td>0.010637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.682198</td>\n",
       "      <td>0.943043</td>\n",
       "      <td>0.147312</td>\n",
       "      <td>-0.657871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.515766</td>\n",
       "      <td>-0.768286</td>\n",
       "      <td>0.361570</td>\n",
       "      <td>1.146278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.607277</td>\n",
       "      <td>-0.003086</td>\n",
       "      <td>-1.499001</td>\n",
       "      <td>1.165728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.226279</td>\n",
       "      <td>-0.177246</td>\n",
       "      <td>-1.379631</td>\n",
       "      <td>-0.639261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.807364</td>\n",
       "      <td>-1.855060</td>\n",
       "      <td>0.325968</td>\n",
       "      <td>1.898831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.438539</td>\n",
       "      <td>-0.728131</td>\n",
       "      <td>-0.009924</td>\n",
       "      <td>0.398360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.497457</td>\n",
       "      <td>-1.506314</td>\n",
       "      <td>-1.557624</td>\n",
       "      <td>0.869043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.945985</td>\n",
       "      <td>-0.519435</td>\n",
       "      <td>-0.510359</td>\n",
       "      <td>-1.077751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.597679</td>\n",
       "      <td>-0.285955</td>\n",
       "      <td>-1.060736</td>\n",
       "      <td>0.608629</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0 -2.346373  0.105651 -0.048027  0.010637\n",
       "1 -0.682198  0.943043  0.147312 -0.657871\n",
       "2  0.515766 -0.768286  0.361570  1.146278\n",
       "3 -0.607277 -0.003086 -1.499001  1.165728\n",
       "4 -1.226279 -0.177246 -1.379631 -0.639261\n",
       "5  0.807364 -1.855060  0.325968  1.898831\n",
       "6  0.438539 -0.728131 -0.009924  0.398360\n",
       "7  1.497457 -1.506314 -1.557624  0.869043\n",
       "8  0.945985 -0.519435 -0.510359 -1.077751\n",
       "9  1.597679 -0.285955 -1.060736  0.608629"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(10, 4))\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以使用 `pd.concat` 函数将多个 `pandas` 对象进行连接："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.346373</td>\n",
       "      <td>0.105651</td>\n",
       "      <td>-0.048027</td>\n",
       "      <td>0.010637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.682198</td>\n",
       "      <td>0.943043</td>\n",
       "      <td>0.147312</td>\n",
       "      <td>-0.657871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.226279</td>\n",
       "      <td>-0.177246</td>\n",
       "      <td>-1.379631</td>\n",
       "      <td>-0.639261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.497457</td>\n",
       "      <td>-1.506314</td>\n",
       "      <td>-1.557624</td>\n",
       "      <td>0.869043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.945985</td>\n",
       "      <td>-0.519435</td>\n",
       "      <td>-0.510359</td>\n",
       "      <td>-1.077751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.597679</td>\n",
       "      <td>-0.285955</td>\n",
       "      <td>-1.060736</td>\n",
       "      <td>0.608629</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0 -2.346373  0.105651 -0.048027  0.010637\n",
       "1 -0.682198  0.943043  0.147312 -0.657871\n",
       "4 -1.226279 -0.177246 -1.379631 -0.639261\n",
       "7  1.497457 -1.506314 -1.557624  0.869043\n",
       "8  0.945985 -0.519435 -0.510359 -1.077751\n",
       "9  1.597679 -0.285955 -1.060736  0.608629"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pieces = [df[:2], df[4:5], df[7:]]\n",
    "\n",
    "pd.concat(pieces)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据库中的 Join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`merge` 可以实现数据库中的 `join` 操作："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   key  lval\n",
      "0  foo     1\n",
      "1  foo     2\n",
      "   key  rval\n",
      "0  foo     4\n",
      "1  foo     5\n"
     ]
    }
   ],
   "source": [
    "left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})\n",
    "right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})\n",
    "\n",
    "print left\n",
    "print right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>lval</th>\n",
       "      <th>rval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>foo</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval  rval\n",
       "0  foo     1     4\n",
       "1  foo     1     5\n",
       "2  foo     2     4\n",
       "3  foo     2     5"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on='key')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### append"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "向 `DataFrame` 中添加行："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.587778</td>\n",
       "      <td>-0.110297</td>\n",
       "      <td>0.602245</td>\n",
       "      <td>1.212597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.551109</td>\n",
       "      <td>0.337387</td>\n",
       "      <td>-0.220919</td>\n",
       "      <td>0.363332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.207373</td>\n",
       "      <td>-0.128394</td>\n",
       "      <td>0.619937</td>\n",
       "      <td>-0.612694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.978282</td>\n",
       "      <td>-1.038170</td>\n",
       "      <td>0.048995</td>\n",
       "      <td>-0.788973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.843893</td>\n",
       "      <td>-1.079021</td>\n",
       "      <td>0.092212</td>\n",
       "      <td>0.485422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.056594</td>\n",
       "      <td>1.831206</td>\n",
       "      <td>1.910864</td>\n",
       "      <td>-1.331739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.487106</td>\n",
       "      <td>-1.495367</td>\n",
       "      <td>0.853440</td>\n",
       "      <td>0.410854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.830852</td>\n",
       "      <td>-0.014893</td>\n",
       "      <td>0.254025</td>\n",
       "      <td>0.197422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  1.587778 -0.110297  0.602245  1.212597\n",
       "1 -0.551109  0.337387 -0.220919  0.363332\n",
       "2  1.207373 -0.128394  0.619937 -0.612694\n",
       "3 -0.978282 -1.038170  0.048995 -0.788973\n",
       "4  0.843893 -1.079021  0.092212  0.485422\n",
       "5 -0.056594  1.831206  1.910864 -1.331739\n",
       "6 -0.487106 -1.495367  0.853440  0.410854\n",
       "7  1.830852 -0.014893  0.254025  0.197422"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D'])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将第三行的值添加到最后："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.587778</td>\n",
       "      <td>-0.110297</td>\n",
       "      <td>0.602245</td>\n",
       "      <td>1.212597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.551109</td>\n",
       "      <td>0.337387</td>\n",
       "      <td>-0.220919</td>\n",
       "      <td>0.363332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.207373</td>\n",
       "      <td>-0.128394</td>\n",
       "      <td>0.619937</td>\n",
       "      <td>-0.612694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.978282</td>\n",
       "      <td>-1.038170</td>\n",
       "      <td>0.048995</td>\n",
       "      <td>-0.788973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.843893</td>\n",
       "      <td>-1.079021</td>\n",
       "      <td>0.092212</td>\n",
       "      <td>0.485422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.056594</td>\n",
       "      <td>1.831206</td>\n",
       "      <td>1.910864</td>\n",
       "      <td>-1.331739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.487106</td>\n",
       "      <td>-1.495367</td>\n",
       "      <td>0.853440</td>\n",
       "      <td>0.410854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.830852</td>\n",
       "      <td>-0.014893</td>\n",
       "      <td>0.254025</td>\n",
       "      <td>0.197422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.978282</td>\n",
       "      <td>-1.038170</td>\n",
       "      <td>0.048995</td>\n",
       "      <td>-0.788973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  1.587778 -0.110297  0.602245  1.212597\n",
       "1 -0.551109  0.337387 -0.220919  0.363332\n",
       "2  1.207373 -0.128394  0.619937 -0.612694\n",
       "3 -0.978282 -1.038170  0.048995 -0.788973\n",
       "4  0.843893 -1.079021  0.092212  0.485422\n",
       "5 -0.056594  1.831206  1.910864 -1.331739\n",
       "6 -0.487106 -1.495367  0.853440  0.410854\n",
       "7  1.830852 -0.014893  0.254025  0.197422\n",
       "8 -0.978282 -1.038170  0.048995 -0.788973"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = df.iloc[3]\n",
    "\n",
    "df.append(s, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>0.773062</td>\n",
       "      <td>0.206503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>1.414609</td>\n",
       "      <td>-0.346719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>0.964174</td>\n",
       "      <td>0.706623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>0.182239</td>\n",
       "      <td>-1.516509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.096255</td>\n",
       "      <td>0.494177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.759471</td>\n",
       "      <td>-0.389213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>-0.257519</td>\n",
       "      <td>-1.411693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>foo</td>\n",
       "      <td>three</td>\n",
       "      <td>-0.109368</td>\n",
       "      <td>0.241862</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B         C         D\n",
       "0  foo    one  0.773062  0.206503\n",
       "1  bar    one  1.414609 -0.346719\n",
       "2  foo    two  0.964174  0.706623\n",
       "3  bar  three  0.182239 -1.516509\n",
       "4  foo    two -0.096255  0.494177\n",
       "5  bar    two -0.759471 -0.389213\n",
       "6  foo    one -0.257519 -1.411693\n",
       "7  foo  three -0.109368  0.241862"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',\n",
    "                          'foo', 'bar', 'foo', 'foo'],\n",
    "                   'B' : ['one', 'one', 'two', 'three',\n",
    "                          'two', 'two', 'one', 'three'],\n",
    "                   'C' : np.random.randn(8),\n",
    "                   'D' : np.random.randn(8)})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "按照 `A` 的值进行分类："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <td>0.837377</td>\n",
       "      <td>-2.252441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <td>1.274094</td>\n",
       "      <td>0.237472</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            C         D\n",
       "A                      \n",
       "bar  0.837377 -2.252441\n",
       "foo  1.274094  0.237472"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('A').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "按照 `A, B` 的值进行分类："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>1.414609</td>\n",
       "      <td>-0.346719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>0.182239</td>\n",
       "      <td>-1.516509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.759471</td>\n",
       "      <td>-0.389213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>0.515543</td>\n",
       "      <td>-1.205191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>-0.109368</td>\n",
       "      <td>0.241862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.867919</td>\n",
       "      <td>1.200800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  C         D\n",
       "A   B                        \n",
       "bar one    1.414609 -0.346719\n",
       "    three  0.182239 -1.516509\n",
       "    two   -0.759471 -0.389213\n",
       "foo one    0.515543 -1.205191\n",
       "    three -0.109368  0.241862\n",
       "    two    0.867919  1.200800"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['A', 'B']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 改变形状"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stack"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "产生一个多 `index` 的 `DataFrame`："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.109174</td>\n",
       "      <td>0.958551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.254743</td>\n",
       "      <td>-0.975924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.132039</td>\n",
       "      <td>-0.119009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.587063</td>\n",
       "      <td>-0.819037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.754123</td>\n",
       "      <td>0.430747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.426544</td>\n",
       "      <td>0.389822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">qux</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.382501</td>\n",
       "      <td>-0.562910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.529287</td>\n",
       "      <td>0.826337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     A         B\n",
       "first second                    \n",
       "bar   one    -0.109174  0.958551\n",
       "      two    -0.254743 -0.975924\n",
       "baz   one    -0.132039 -0.119009\n",
       "      two     0.587063 -0.819037\n",
       "foo   one    -0.754123  0.430747\n",
       "      two    -0.426544  0.389822\n",
       "qux   one    -0.382501 -0.562910\n",
       "      two    -0.529287  0.826337"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',\n",
    "                     'foo', 'foo', 'qux', 'qux'],\n",
    "                    ['one', 'two', 'one', 'two',\n",
    "                     'one', 'two', 'one', 'two']]))\n",
    "\n",
    "index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])\n",
    "df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`stack` 方法将 `columns` 变成一个新的 `index` 部分："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first  second   \n",
       "bar    one     A   -0.109174\n",
       "               B    0.958551\n",
       "       two     A   -0.254743\n",
       "               B   -0.975924\n",
       "baz    one     A   -0.132039\n",
       "               B   -0.119009\n",
       "       two     A    0.587063\n",
       "               B   -0.819037\n",
       "dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df[:4]\n",
    "\n",
    "stacked = df2.stack()\n",
    "\n",
    "stacked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以使用 `unstack()` 将最后一级 `index` 放回 `column`："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.109174</td>\n",
       "      <td>0.958551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.254743</td>\n",
       "      <td>-0.975924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.132039</td>\n",
       "      <td>-0.119009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.587063</td>\n",
       "      <td>-0.819037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     A         B\n",
       "first second                    \n",
       "bar   one    -0.109174  0.958551\n",
       "      two    -0.254743 -0.975924\n",
       "baz   one    -0.132039 -0.119009\n",
       "      two     0.587063 -0.819037"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "也可以指定其他的级别："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>second</th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.109174</td>\n",
       "      <td>-0.254743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.958551</td>\n",
       "      <td>-0.975924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.132039</td>\n",
       "      <td>0.587063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.119009</td>\n",
       "      <td>-0.819037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "second        one       two\n",
       "first                      \n",
       "bar   A -0.109174 -0.254743\n",
       "      B  0.958551 -0.975924\n",
       "baz   A -0.132039  0.587063\n",
       "      B -0.119009 -0.819037"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 时间序列"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "金融分析中常用到时间序列数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-06    1.096788\n",
       "2012-03-07    0.029678\n",
       "2012-03-08    0.511461\n",
       "2012-03-09   -0.332369\n",
       "2012-03-10    1.720321\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D')\n",
    "ts = pd.Series(np.random.randn(len(rng)), rng)\n",
    "\n",
    "ts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "标准时间表示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-06 00:00:00+00:00    1.096788\n",
       "2012-03-07 00:00:00+00:00    0.029678\n",
       "2012-03-08 00:00:00+00:00    0.511461\n",
       "2012-03-09 00:00:00+00:00   -0.332369\n",
       "2012-03-10 00:00:00+00:00    1.720321\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc = ts.tz_localize('UTC')\n",
    "\n",
    "ts_utc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "改变时区表示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-05 19:00:00-05:00    1.096788\n",
       "2012-03-06 19:00:00-05:00    0.029678\n",
       "2012-03-07 19:00:00-05:00    0.511461\n",
       "2012-03-08 19:00:00-05:00   -0.332369\n",
       "2012-03-09 19:00:00-05:00    1.720321\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc.tz_convert('US/Eastern')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categoricals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>raw_grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id raw_grade\n",
       "0   1         a\n",
       "1   2         b\n",
       "2   3         b\n",
       "3   4         a\n",
       "4   5         a\n",
       "5   6         e"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"id\":[1,2,3,4,5,6], \"raw_grade\":['a', 'b', 'b', 'a', 'a', 'e']})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以将 `grade` 变成类别："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    b\n",
       "3    a\n",
       "4    a\n",
       "5    e\n",
       "Name: grade, dtype: category\n",
       "Categories (3, object): [a, b, e]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"grade\"] = df[\"raw_grade\"].astype(\"category\")\n",
    "\n",
    "df[\"grade\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将类别的表示转化为有意义的字符："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    very good\n",
       "1         good\n",
       "2         good\n",
       "3    very good\n",
       "4    very good\n",
       "5     very bad\n",
       "Name: grade, dtype: category\n",
       "Categories (3, object): [very good, good, very bad]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"grade\"].cat.categories = [\"very good\", \"good\", \"very bad\"]\n",
    "\n",
    "df[\"grade\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "添加缺失的类别："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    very good\n",
       "1         good\n",
       "2         good\n",
       "3    very good\n",
       "4    very good\n",
       "5     very bad\n",
       "Name: grade, dtype: category\n",
       "Categories (5, object): [very bad, bad, medium, good, very good]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"grade\"] = df[\"grade\"].cat.set_categories([\"very bad\", \"bad\", \"medium\", \"good\", \"very good\"])\n",
    "df[\"grade\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用 `grade` 分组："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grade\n",
       "very bad     1\n",
       "bad          0\n",
       "medium       0\n",
       "good         2\n",
       "very good    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"grade\").size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用 `ggplot` 风格："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Series` 绘图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FBQVs2rSJ+fPns3nzZgoKCvryNLGjsgwc0j0TCmrCSajRx6P/vAI+XwcH9sKI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3dc69a4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))\n",
    "\n",
    "p = ts.cumsum().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`DataFrame` 按照 `columns` 绘图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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44+Pjyc3NBcBut5OVlUV9fT0bN25k3rx5AMyfP58NG47O1e90Quo6smSvep17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\nAGMIPwAY8wFNd7R2zEaFBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3dc4fb310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,\n",
    "                  columns=['A', 'B', 'C', 'D'])\n",
    "\n",
    "df.cumsum().plot()\n",
    "p = plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 文件读写"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "写入文件："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_csv('foo.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从文件中读取："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>-1.011554</td>\n",
       "      <td>1.200283</td>\n",
       "      <td>-0.310949</td>\n",
       "      <td>-1.060734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-01-02</td>\n",
       "      <td>-1.030894</td>\n",
       "      <td>0.660518</td>\n",
       "      <td>-0.214002</td>\n",
       "      <td>-0.422014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-01-03</td>\n",
       "      <td>-0.488692</td>\n",
       "      <td>1.709209</td>\n",
       "      <td>-0.602208</td>\n",
       "      <td>1.115456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-01-04</td>\n",
       "      <td>-0.440243</td>\n",
       "      <td>0.826692</td>\n",
       "      <td>0.321648</td>\n",
       "      <td>-0.351698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-01-05</td>\n",
       "      <td>-0.165684</td>\n",
       "      <td>1.297303</td>\n",
       "      <td>0.817233</td>\n",
       "      <td>0.174767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0         A         B         C         D\n",
       "0  2000-01-01 -1.011554  1.200283 -0.310949 -1.060734\n",
       "1  2000-01-02 -1.030894  0.660518 -0.214002 -0.422014\n",
       "2  2000-01-03 -0.488692  1.709209 -0.602208  1.115456\n",
       "3  2000-01-04 -0.440243  0.826692  0.321648 -0.351698\n",
       "4  2000-01-05 -0.165684  1.297303  0.817233  0.174767"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('foo.csv').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### hdf5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "写入文件："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.to_hdf(\"foo.h5\", \"df\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "读取文件："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>-1.011554</td>\n",
       "      <td>1.200283</td>\n",
       "      <td>-0.310949</td>\n",
       "      <td>-1.060734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>-1.030894</td>\n",
       "      <td>0.660518</td>\n",
       "      <td>-0.214002</td>\n",
       "      <td>-0.422014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-0.488692</td>\n",
       "      <td>1.709209</td>\n",
       "      <td>-0.602208</td>\n",
       "      <td>1.115456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>-0.440243</td>\n",
       "      <td>0.826692</td>\n",
       "      <td>0.321648</td>\n",
       "      <td>-0.351698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-0.165684</td>\n",
       "      <td>1.297303</td>\n",
       "      <td>0.817233</td>\n",
       "      <td>0.174767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2000-01-01 -1.011554  1.200283 -0.310949 -1.060734\n",
       "2000-01-02 -1.030894  0.660518 -0.214002 -0.422014\n",
       "2000-01-03 -0.488692  1.709209 -0.602208  1.115456\n",
       "2000-01-04 -0.440243  0.826692  0.321648 -0.351698\n",
       "2000-01-05 -0.165684  1.297303  0.817233  0.174767"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_hdf('foo.h5','df').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### excel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "写入文件："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.to_excel('foo.xlsx', sheet_name='Sheet1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "读取文件："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>-1.011554</td>\n",
       "      <td>1.200283</td>\n",
       "      <td>-0.310949</td>\n",
       "      <td>-1.060734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>-1.030894</td>\n",
       "      <td>0.660518</td>\n",
       "      <td>-0.214002</td>\n",
       "      <td>-0.422014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-0.488692</td>\n",
       "      <td>1.709209</td>\n",
       "      <td>-0.602208</td>\n",
       "      <td>1.115456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>-0.440243</td>\n",
       "      <td>0.826692</td>\n",
       "      <td>0.321648</td>\n",
       "      <td>-0.351698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-0.165684</td>\n",
       "      <td>1.297303</td>\n",
       "      <td>0.817233</td>\n",
       "      <td>0.174767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2000-01-01 -1.011554  1.200283 -0.310949 -1.060734\n",
       "2000-01-02 -1.030894  0.660518 -0.214002 -0.422014\n",
       "2000-01-03 -0.488692  1.709209 -0.602208  1.115456\n",
       "2000-01-04 -0.440243  0.826692  0.321648 -0.351698\n",
       "2000-01-05 -0.165684  1.297303  0.817233  0.174767"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "清理生成的临时文件："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "\n",
    "for f in glob.glob(\"foo*\"):\n",
    "    os.remove(f)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
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   "name": "python2"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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